Analisis Sentimen Tweet Pengguna Twitter Terkait Diabetes Menggunakan Metode Naive Bayes - Afiyatar Asyer Asyer; Magdalena A. Ineke Pakereng Ineke Pakereng (STMIK Banjarbaru, 2023)
Diabetes is a disease that has serious implications for the health and overall well-being of individuals, and there are diverse perspectives within society regarding this condition, leading to prolonged debates. Twitter is a popular platform where public opinions are often shared, and sentiment analysis on Twitter can provide insights into users' perceptions of diabetes, whether positive, negative, or neutral, to understand the health literacy regarding obesity among Twitter users. Sentiment analysis was conducted using text mining methodology, with a total of 26,038 crawled tweets, resulting in a sample of 4,635 data points. Through the Naïve Bayes algorithm, the sentiment analysis yielded 22% positive sentiment, 14% negative sentiment, and 64% neutral sentiment. The accuracy rate achieved was 87%. The sentiment analysis of tweets related to diabetes using text mining leaned more towards neutral sentiment than negative or positive sentiment. The accuracy of the Naïve Bayes algorithm falls within the category of